"""Run inference with a YOLOv5 model on images, videos, directories, streams

Usage:
    $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
"""

import argparse
import concurrent.futures
import os
import sys
import time
from io import BufferedReader, BytesIO
from pathlib import Path
from flaskTest import encapsulationDataObject
from yolo.requestDrawing import requestDrawPicture
import cv2
import torch
import torch.backends.cudnn as cudnn
from utils.torch_utils import load_classifier, select_device, time_sync

FILE = Path(__file__).absolute()
sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
    apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import plot_one_box
import uuid


@torch.no_grad()
def run(weights='yolov5s.pt',  # model.pt path(s)
        source='0',  # file/dir/URL/glob, 0 for webcam
        imgsz=640,  # inference size (pixels)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=True,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project='runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        ):
    global ch_text, color_single, count, object

    save_img = not nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # 初始化
    set_logging()
    device = select_device(device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA

    # 负载模型
    w = weights[0] if isinstance(weights, list) else weights
    classify, pt, onnx = False, w.endswith('.pt'), w.endswith('.onnx')  # inference type
    stride, names = 64, [f'class{i}' for i in range(1000)]  # assign defaults
    if pt:
        model = attempt_load(weights, map_location=device)  # load FP32 model
        stride = int(model.stride.max())  # model stride
        names = model.module.names if hasattr(model, 'module') else model.names  # get class names
        if half:
            model.half()  # to FP16
        if classify:  # second-stage classifier
            modelc = load_classifier(name='resnet50', n=2)  # initialize
            modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
    elif onnx:
        check_requirements(('onnx', 'onnxruntime'))
        import onnxruntime
        session = onnxruntime.InferenceSession(w, None)
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # 数据加载器
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    if pt and device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    # 中英文对照关系 检测到物体却没有中英文对照 默认是物体
    map = {'person': '人', 'laptop': '笔记本电脑', 'chair': '椅子', 'bus': '巴士', 'tie': '领带', 'fire hydrant': '消防栓'}

    for path, img, im0s, vid_cap in dataset:

        if pt:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 到 fp1632
        elif onnx:
            img = img.astype('float32')
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if len(img.shape) == 3:
            img = img[None]  # expand for batch dim

        # 推理
        t1 = time_sync()
        if pt:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            pred = model(img, augment=augment, visualize=visualize)[0]
        elif onnx:
            pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))

        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        t2 = time_sync()

        # 二级分类器（可选）
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # 过程预测
        for i, det in enumerate(pred):
            # flag = True
            # 每张图像的检测数
            # im0即视频这一帧生成的图片 类型 ndarray
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            im0.copy() if save_crop else im0  # for save_crop

            # 1秒识别一次测试 todo 30帧识别一次
            if frame % 30 == 0:
                data = set()
                # 是否检测到物体
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
                    # 写出结果
                    object = set()
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
                        if names[int(c)] in map:
                            object.add(names[int(c)])
                    data = object
                    print("检测到的结果", data)
                    for *xyxy, conf, cls in det:
                        if save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # 标准化xywh
                            with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
                                file.write(('%g ' * 5 + '\n') % (cls, *xywh))  # 标签格式
                    # 写入结果
                    for *xyxy, conf, cls in reversed(det):
                        if save_img or save_crop or view_img:  # 将 bbox 添加到图像
                            # 根据像素确定长宽和对角线像素长度
                            c1, c2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
                            weight = abs(c2[1] - c1[1])
                            length = abs(c2[0] - c1[0])
                            diagonal = round(((c2[0] - c1[0]) ** 2 + (c2[1] - c1[1]) ** 2) ** 0.5, 2)
                            areas = round(abs(c2[1] - c1[1]) * abs(c2[0] - c1[0]), 2)
                            # 设置固定颜色
                            color_dict = {'1': [220, 20, 60], '2': [75, 195, 185], '3': [255, 165, 0],
                                          '4': [60, 20, 220]}
                            # 物体识别 若英文映射中文中 中文没有则默认显示物体
                            ch_text = '%s' % ('object')
                            color_single = [220, 20, 60]
                            # 中文输出
                            if names[int(cls)] in map:
                                ch_text = '%s' % (names[int(cls)])
                                # ，%.2f , conf
                                color_single = color_dict['1']

                            # 发送请求进行绘图
                            #  todo im0:ndarray 怎样进行post传输
                            im0=requestDrawPicture(xyxy, im0, label='4', ch_text=ch_text, color=color_single,
                                               line_thickness=3)
                            # im0 = plot_one_box(xyxy, im0, label='4', ch_text=ch_text, color=color_single,
                            #                    line_thickness=3)
                # 如果没有识别到map中文映射里的东西(即使识别到了默认的物体) 就不生成图片发送给Java接口
                if len(data) > 0:
                    uuidGenerator = (str(uuid.uuid4())[0:6])
                    importPath = 'F:/image/' + uuidGenerator + '.jpg'
                    # 本地生成图片
                    if not os.path.exists(importPath):
                        with open(importPath, mode='w', encoding='utf-8'):
                            print(importPath + "文件创建成功")
                    cv2.imwrite(importPath, im0)
                    # Print time (inference + NMS)
                    print(f'{s}Done. ({t2 - t1:.3f}s`)')
                    # 调用接口 将生成的图片发送给Java
                    encapsulationDataObject(importPath, object)
        # Stream results
        # if view_img:
        #     cv2.imshow(str(p), im0)
        #     cv2.waitKey(1)  # 1 millisecond

        # Save results (image with detections)

    #         if save_img:
    #             if dataset.mode == 'image':
    #                 cv2.imwrite(save_path, im0)
    #             else:  # 'video' or 'stream'
    #                 if vid_path[i] != save_path:  # new video
    #                     vid_path[i] = save_path
    #                     if isinstance(vid_writer[i], cv2.VideoWriter):
    #                         vid_writer[i].release()  # release previous video writer
    #                     if vid_cap:  # video
    #                         fps = vid_cap.get(cv2.CAP_PROP_FPS)
    #                         w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    #                         h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    #                     else:  # stream
    #                         fps, w, h = 30, im0.shape[1], im0.shape[0]
    #                         save_path += '.mp4'
    #                     vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
    #                 vid_writer[i].write(im0)
    #
    # if save_txt or save_img:
    #     s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
    #     print(f"Results saved to {save_dir}{s}")
    #
    # if update:
    #     strip_optimizer(weights)  # update model (to fix SourceChangeWarning)

    print(f'Done. ({time.time() - t0:.3f}s)')


def parse_opt(source):
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default=source, help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', default=True, action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    opt = parser.parse_args()
    return opt


def main(opt):
    print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    # source={'G:/HP/Pictures/3.MOV','G:/HP/Pictures/2.MOV'}
    source = 'G:/HP/Pictures/3.MOV'
    opt = parse_opt(source)
    main(opt)
    # with concurrent.futures.ThreadPoolExecutor() as pool:
    #     start = time.time()
    #     for s in source:
    #         opt = parse_opt(s)
    #         pool.submit(main, opt)
